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Development and Improvement of Tissue Simulation Toolkit

Development and Improvement of Tissue Simulation Toolkit
组织模拟工具包的开发和改进
批准号:
7234925
负责人:
James Alexander Glazier
金额:
$53.0万
依托单位国家:
美国
项目类别:
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-09-28 至 2010-08-31

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中文摘要
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英文摘要
DESCRIPTION (provided by applicant): Project Summary: We propose to develop an open-source modeling environment, the Tissue-Simulation Environment (TSE), for the cell-based modeling of the structure and behavior of tissues and organs using the Cellular Potts Model (CPM) formalism. While groups worldwide have used the CPM methods to model biomedical phenomena, including vascular development, tumor growth, wound healing and the immune system, no open-source package currently supports this class of model. Our Specific Aim is that the TSE should provide modeling capability comparable to or better than other CPM simulations and be usable by and attractive to a broad community of biomodelers and experimentalists. To accomplish this aim we will provide a highly expandable core simulation engine, numerous validated sample biological models, a flexible and intuitive graphical user interface with visualization capability, extensive documentation and a number of modules describing biological processes not included in other CPM simulations. In addition we will provide user-training workshops and conduct ongoing user-need surveys. In response to the NIH Roadmap's call for interoperability and multiscale simulations, the TSE will also provide connectivity to other major subcellular and tissue-level software packages through a program of markup- language development and support. The TSE will also provide a mechanism to collect, validate and release user-submitted modules, models, bug reports and fixes to ensure that it develops rapidly and reliably in a way responsive to its users. Relevance: Simulations of cell interactions during embryonic development, wound healing, and disease can play an important role in experimental biomedical studies, help with the interpretation of experimental results, suggest experiments, predict experimental outcomes and lead to deeper understanding of fundamental biological mechanisms, thus expediting the understanding of diseases and the development of treatment strategies.
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